Application of Deep Learning to Spectroscopic Features of the Balmer-Alpha Line for Hydrogen Isotopic Ratio Determination in Tokamaks - Aix-Marseille Université Access content directly
Journal Articles Applied Sciences Year : 2022

Application of Deep Learning to Spectroscopic Features of the Balmer-Alpha Line for Hydrogen Isotopic Ratio Determination in Tokamaks

M. Koubiti
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Malo Kerebel
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Abstract

We propose in this paper the use of artificial intelligence, especially deep learning algorithms, for the isotopic ratio determination for hydrogen-deuterium mixtures. Our approach is based on the Balmer-α line emitted by hydrogen and deuterium, but unlike the standard method, it does not consist of fitting the Hα/Dα line spectra. Instead, only some basic spectroscopic features such as the line peak-to-dip wavelength separation, peak-to-peak and dip-to-peak intensity ratios of the Zeeman-Doppler-broadened Hα/Dα line spectra are used by the regression algorithm for training. We demonstrate the proof-of-principle of our approach by applying deep learning from the open-access machine-learning platform TensorFlow to Hα/Dα line profiles, which we have synthetized with predetermined parameters such as neutral temperatures, the magnetic field strength and the H/(H + D) isotopic ratio. The used regression algorithm allowed us to retrieve with a good accuracy the isotopic ratios used for the synthetized line profiles.
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hal-03798754 , version 1 (05-10-2022)

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M. Koubiti, Malo Kerebel. Application of Deep Learning to Spectroscopic Features of the Balmer-Alpha Line for Hydrogen Isotopic Ratio Determination in Tokamaks. Applied Sciences, 2022, 12, pp.9891. ⟨10.3390/app12199891⟩. ⟨hal-03798754⟩

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